






"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE   =   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE   =   {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__SCREAMING_SNAKE_CASE   =   {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__SCREAMING_SNAKE_CASE   =   {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Any      =  VOCAB_FILES_NAMES
					lowercase_    :				str      =  PRETRAINED_VOCAB_FILES_MAP
					lowercase_    :				int      =  PRETRAINED_INIT_CONFIGURATION
					lowercase_    :				List[Any]      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_    :				Tuple      =  SqueezeBertTokenizer
					def __init__(							self							:	Tuple     , UpperCAmelCase							:	List[Any]=None     , UpperCAmelCase							:	Union[str, Any]=None     , UpperCAmelCase							:	Tuple=True     , UpperCAmelCase							:	int="[UNK]"     , UpperCAmelCase							:	Optional[int]="[SEP]"     , UpperCAmelCase							:	List[str]="[PAD]"     , UpperCAmelCase							:	Tuple="[CLS]"     , UpperCAmelCase							:	int="[MASK]"     , UpperCAmelCase							:	Any=True     , UpperCAmelCase							:	int=None     , **UpperCAmelCase							:	str     , )	->       Dict:
											super().__init__(
											    UpperCAmelCase     , tokenizer_file=UpperCAmelCase     , do_lower_case=UpperCAmelCase     , unk_token=UpperCAmelCase     , sep_token=UpperCAmelCase     , pad_token=UpperCAmelCase     , cls_token=UpperCAmelCase     , mask_token=UpperCAmelCase     , tokenize_chinese_chars=UpperCAmelCase     , strip_accents=UpperCAmelCase     , **UpperCAmelCase     , )

											lowerCAmelCase   :str						=			json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
											if (
											    normalizer_state.get('lowercase'     , UpperCAmelCase   ) != do_lower_case
											    or normalizer_state.get('strip_accents'     , UpperCAmelCase   ) != strip_accents
											    or normalizer_state.get('handle_chinese_chars'     , UpperCAmelCase   ) != tokenize_chinese_chars
											):
																	lowerCAmelCase   :Optional[int]						=			getattr(UpperCAmelCase     , normalizer_state.pop('type'   )   )
																	lowerCAmelCase   :Tuple						=			do_lower_case
																	lowerCAmelCase   :Optional[Any]						=			strip_accents
																	lowerCAmelCase   :Union[str, Any]						=			tokenize_chinese_chars
																	lowerCAmelCase   :Any						=			normalizer_class(**UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			do_lower_case
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	Optional[Any]=None   )	->       List[Any]:
											lowerCAmelCase   :int						=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

											return output
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	List[int]     , UpperCAmelCase							:	Optional[List[int]] = None   )	->       List[int]:
											lowerCAmelCase   :Any						=			[self.sep_token_id]
											lowerCAmelCase   :str						=			[self.cls_token_id]
											if token_ids_a is None:
																	return len(cls + token_ids_a + sep   ) * [0]
											return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Optional[str] = None   )	->       Tuple[str]:
											lowerCAmelCase   :str						=			self._tokenizer.model.save(UpperCAmelCase     , name=UpperCAmelCase   )
											return tuple(UpperCAmelCase   )







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE   =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AdaptiveEmbedding',
		    'TransfoXLForSequenceClassification',
		    'TransfoXLLMHeadModel',
		    'TransfoXLModel',
		    'TransfoXLPreTrainedModel',
		    'load_tf_weights_in_transfo_xl',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAdaptiveEmbedding',
		    'TFTransfoXLForSequenceClassification',
		    'TFTransfoXLLMHeadModel',
		    'TFTransfoXLMainLayer',
		    'TFTransfoXLModel',
		    'TFTransfoXLPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)